OpenClaw

idx-cma-report

Generate comparative market analysis (CMA) and home valuation reports from IDX listing data and selected comparable properties. Use when a user wants to pick comps, estimate a market value range, produce seller-facing home evaluation reports, or publish an interactive CMA experience via Google Gemini Canvas or Google AI Studio.

danielfoch0 来源安装数☆ 0 仓库 Stars

查看来源 ↗安装与使用 SKILL.md ↗

安装与使用

请根据来源仓库和你的 AI 工具说明安装此技能。先阅读技能文件和依赖要求，再添加到工作流。

SkillHub CLI

skillhub install danielfoch/idx-cma-report

打开来源仓库，查看说明和兼容环境后，按来源提供的命令或步骤安装。

将来源链接提供给你的 Agent，让它先读取说明、核对依赖，并向你说明将执行的安装步骤。

查看来源 ↗使用指南

技能说明

IDX CMA Report

Use this skill to turn subject-property data and IDX comparables into a defensible CMA package with:

Structured valuation calculations
A written report for agent/client review
An interactive handoff prompt for Google Gemini Canvas / Google AI Studio

Workflow

1. Gather Data Through IDX MCP/CLI

Use the IDX MCP/CLI skill already available in the environment to pull:

Subject property details
Candidate comparable listings (closed/pending/active based on user preference)

Ask the user which comps to include when the choice is ambiguous. Keep 3 to 8 comps unless the user requests otherwise.

Normalize data to JSON using the schema in references/cma-input-schema.md.

2. Build CMA Outputs

Run:

python3 scripts/build_cma.py \
  --subject subject.json \
  --comps comps.json \
  --output-dir cma-output

The script produces:

cma-output/cma_report.md (summary report)
cma-output/cma_data.json (calculation payload)
cma-output/interactive_local.html (local interactive view)
cma-output/gemini_canvas_prompt.md (prompt for Google tools)

3. Review and Explain Adjustments

Before final delivery:

Show the comp set used
Show estimated range and central estimate
Explain assumptions and major adjustments in plain language
Flag missing/low-quality fields that weaken confidence

Use references/valuation-guidelines.md for adjustment defaults and confidence guidance.

4. Publish Interactive Version in Gemini

Use cma-output/gemini_canvas_prompt.md as the base prompt. Then:

Open Google AI Studio or Gemini Canvas.
Paste the generated prompt and provide cma_data.json.
Ask for an interactive CMA web app with:
- Comp table with sorting/filtering
- Map-ready data fields (if lat/lng present)
- Value-range visualization
- Notes panel explaining adjustments
Request hosted/shareable output if available in the chosen Google tool.

See `references/gemini-ca...

来源信息

Source: github
Repository: openclaw/skills
Path: skills/danielfoch/idx-cma-report
Version: —
Commit: 7e7e001f8208deceec8d6bfbfee2bb9e8b21cae6
分类来源: rules
状态: active
可见性: public

这些信息来自收录时的来源分析，可能滞后；使用前请核对最新来源文件。

来源内容

SKILL.md

# IDX CMA Report

Use this skill to turn subject-property data and IDX comparables into a defensible CMA package with:

- Structured valuation calculations
- A written report for agent/client review
- An interactive handoff prompt for Google Gemini Canvas / Google AI Studio

## Workflow

### 1. Gather Data Through IDX MCP/CLI
Use the IDX MCP/CLI skill already available in the environment to pull:

- Subject property details
- Candidate comparable listings (closed/pending/active based on user preference)

Ask the user which comps to include when the choice is ambiguous. Keep 3 to 8 comps unless the user requests otherwise.

Normalize data to JSON using the schema in `references/cma-input-schema.md`.

### 2. Build CMA Outputs
Run:

```bash
python3 scripts/build_cma.py \
--subject subject.json \
--comps comps.json \
--output-dir cma-output
```

The script produces:

- `cma-output/cma_report.md` (summary report)
- `cma-output/cma_data.json` (calculation payload)
- `cma-output/interactive_local.html` (local interactive view)
- `cma-output/gemini_canvas_prompt.md` (prompt for Google tools)

### 3. Review and Explain Adjustments
Before final delivery:

- Show the comp set used
- Show estimated range and central estimate
- Explain assumptions and major adjustments in plain language
- Flag missing/low-quality fields that weaken confidence

Use `references/valuation-guidelines.md` for adjustment defaults and confidence guidance.

### 4. Publish Interactive Version in Gemini
Use `cma-output/gemini_canvas_prompt.md` as the base prompt. Then:

1. Open [Google AI Studio](https://aistudio.google.com/) or Gemini Canvas.
2. Paste the generated prompt and provide `cma_data.json`.
3. Ask for an interactive CMA web app with:
- Comp table with sorting/filtering
- Map-ready data fields (if lat/lng present)
- Value-range visualization
- Notes panel explaining adjustments
4. Request hosted/shareable output if available in the chosen Google tool.